Issues related to modeling the body mass index-mortality association: the shape of the association and the effects of smoking status
ABSTRACT Research on the relationship between body mass index (BMI) and mortality has led to conflicting results; a lack of agreement about how to adjust for confounders, such as smoking status, has added to the problem. Complicating such analyses is the fact that the BMI-mortality association is not a symmetric quadratic relationship; the distribution tends to be skewed to the right, causing the optimal BMI--where mortality is at a minimum--to be overestimated. One way to overcome this problem is by transformation of the BMI distribution to normality. The authors suggest several approaches for doing so, including the use of 1/BMI, or lean body mass index, instead of BMI in modeling. Data sets on 50 cohorts from approximately 30 international studies were used to examine the association (direct, inverse, quadratic or none) between BMI and mortality and to investigate the possible interaction of smoking status. Of the 50 cohorts, 36 showed a quadratic association between BMI and mortality, 10 showed no association and 1 showed a direct association between lean BMI and mortality. Only three cohorts showed a significant interaction between BMI and smoking, which was approximately what one would expect from a 5% significance test, even if no interaction existed. The association between BMI and mortality is not changed when smoking status is ignored in a model or when data on smokers are excluded from analysis. The methodology used in this study could be extended to look for other interactions.
Full-textDOI: · Available from: Ramón Angel Durazo-Arvizu, Jul 07, 2014
- SourceAvailable from: Rafael Alfonso-Cristancho
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- "Analyses were stratified by gender because the biological process by which men and women gain and maintain weight is different . We adjusted for smoking status because it confounded the BMI-mortality relationship, which if ignored may result in overestimation of the BMI associated with minimum mortality . Sample adult weights from the NHIS, which denoted the inverse probability of inclusion into the sample were used within the logistic regression model to correct for potential biases resulting from the NHIS sampling design. "
ABSTRACT: Many previous studies estimating the relationship between body mass index (BMI) and mortality impose assumptions regarding the functional form for BMI and result in conflicting findings. This study investigated a flexible data driven modelling approach to determine the nonlinear and asymmetric functional form for BMI used to examine the relationship between mortality and obesity. This approach was then compared against other commonly used regression models. This study used data from the National Health Interview Survey, between 1997 and 2000. Respondents were linked to the National Death Index with mortality follow-up through 2005. We estimated 5-year all-cause mortality for adults over age 18 using the logistic regression model adjusting for BMI, age and smoking status. All analyses were stratified by sex. The multivariable fractional polynomials (MFP) procedure was employed to determine the best fitting functional form for BMI and evaluated against the model that includes linear and quadratic terms for BMI and the model that groups BMI into standard weight status categories using a deviance difference test. Estimated BMI-mortality curves across models were then compared graphically. The best fitting adjustment model contained the powers -1 and -2 for BMI. The relationship between 5-year mortality and BMI when estimated using the MFP approach exhibited a J-shaped pattern for women and a U-shaped pattern for men. A deviance difference test showed a statistically significant improvement in model fit compared to other BMI functions. We found important differences between the MFP model and other commonly used models with regard to the shape and nadir of the BMI-mortality curve and mortality estimates. The MFP approach provides a robust alternative to categorization or conventional linear-quadratic models for BMI, which limit the number of curve shapes. The approach is potentially useful in estimating the relationship between the full spectrum of BMI values and other health outcomes, or costs.BMC Medical Research Methodology 12/2011; 11(1):175. DOI:10.1186/1471-2288-11-175 · 2.27 Impact Factor
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- "Thus, the mean BMI decline for each subsequent cohort entering adolescence from 2010 to 2028 will be equivalent to the annual increase in mean BMI that was observed from 1985 to 2003. In our model, this cohort effect was modeled as a trend in the mean of the inverse of BMI, following other published research (13,14), increasing by 3.1 × 10-4 annually for boys aged 12 and increasing by 4.5 × 10-4 annually for girls aged 12. This increase in the mean of the inverse of BMI translates into an annual decrease in BMI of approximately 0.19 kg/m2 for boys with an initial BMI of 25.0 kg/m2 and a decrease of 0.18 kg/m2 for girls with an initial BMI of 25.0 kg/m2. "
ABSTRACT: Setting a goal for controlling type 2 diabetes is important for planning health interventions. The purpose of this study was to explore what may be a feasible goal for type 2 diabetes prevention in California. We used the UCLA Health Forecasting Tool, a microsimulation model that simulates individual life courses in the population, to forecast the prevalence of type 2 diabetes in California's adult population in 2020. The first scenario assumes no further increases in average body mass index (BMI) for cohorts entering adolescence after 2003. The second scenario assumes a gradual BMI decrease for children entering adolescence after 2010. The third scenario builds on the second by extending the same BMI decrease to people aged 12 to 65 years. The fourth scenario builds on the third by eliminating racial/ethnic disparities in physical activity. We found the predicted diabetes prevalence of the first, second, third, and fourth scenarios in 2020 to be 9.93%, 9.91%, 9.76%, and 9.77%, respectively. We found obesity prevalence for type 2 diabetes patients in 2020 to be 34.2%, 34.0%, 25.7%, and 25.6% for the 4 scenarios. Life expectancy in the third (80.56 y) and fourth (80.94 y) scenarios compared favorably with that of the first (80.32 y) and second (80.32 y) scenarios. For the next 10 years, behavioral risk factor modifications are more likely to affect obesity prevalence and life expectancy in the general population and obesity prevalence among diabetic patients than to alter type 2 diabetes prevalence in the general population. We suggest setting more specific goals for reducing the prevalence of diabetes, such as reducing obesity-related diabetes complications, which may be more feasible and easier to evaluate than the omnibus goal of lowering overall type 2 diabetes prevalence by 2020.Preventing chronic disease 07/2011; 8(4):A80. · 2.12 Impact Factor
- International journal of obesity (2005) 09/2008; 32 Suppl 3:S1-3. DOI:10.1038/ijo.2008.80 · 5.00 Impact Factor